Aproori算法用代码实现
时间: 2024-05-12 14:17:05 浏览: 8
以下是Aproori算法的Python代码实现:
```python
def count_support(data, itemset):
count = 0
for d in data:
if itemset.issubset(d):
count += 1
return count
def apriori(data, min_support=0.5):
# 初始化频繁项集和候选项集
freq_sets = []
cand_sets = [frozenset([i]) for i in data[0]]
n_samples = len(data)
while cand_sets:
# 计算候选项集的支持度
new_sets = []
for c in cand_sets:
support = count_support(data, c) / n_samples
if support >= min_support:
freq_sets.append(c)
new_sets.append(c)
# 生成新的候选项集
cand_sets = []
for i in range(len(new_sets)):
for j in range(i+1,len(new_sets)):
union_set = new_sets[i].union(new_sets[j])
if len(union_set) == len(new_sets[i])+1:
cand_sets.append(union_set)
return freq_sets
```
这段代码实现了Aproori算法来寻找频繁项集。其中,`data` 是输入数据集,每个样本是一个项集;`min_support` 是支持度阈值。函数返回满足条件的频繁项集。
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